Department of Electrical & Computer Engineering, North South University, Bashundhara, Dhaka, 1229, Bangladesh.
Comput Biol Med. 2021 Dec;139:104931. doi: 10.1016/j.compbiomed.2021.104931. Epub 2021 Oct 13.
Invasive ductal carcinoma (IDC) breast cancer is a significant health concern for women all around the world and early detection of the disease may increase the survival rate in patients. Therefore, Computer-Aided Diagnosis (CAD) based systems can assist pathologists to detect the disease early. In this study, we present an ensemble model to detect IDC using DenseNet-121 and DenseNet-169 followed by test time augmentation (TTA). The model achieved a balanced accuracy of 92.70% and an F1-score of 95.70% outperforming the current state-of-the-art. Comparative analysis against various pre-trained deep learning models and preprocessing methods have been carried out. Qualitative analysis has also been conducted on the test dataset. After the detection of IDC breast cancer, it is important to grade it for further treatment. In our study, we also propose an ensemble model for the grading of IDC using the pre-trained DenseNet-121, DenseNet-201, ResNet-101v2, and ResNet-50 architectures. The model is inferred from two validation cohorts. For the patch-level classification, the model yielded an overall accuracy of 69.31%, 75.07%, 61.85%, and 60.50% on one validation cohort and 62.44%, 79.14%, 76.62%, and 71.05% on the second validation cohort for 4×, 10×, 20×, and 40× magnified images respectively. The same architecture is further validated using a different IDC dataset where it achieved an overall accuracy of 90.07%. The performance of the models on the detection and grading of IDC shows that they can be useful to help pathologists detect and grade the disease.
浸润性导管癌（IDC）乳腺癌是全世界女性面临的重大健康问题，早期发现疾病可能会提高患者的生存率。因此，基于计算机辅助诊断（CAD）的系统可以帮助病理学家早期发现疾病。在这项研究中，我们提出了一种使用 DenseNet-121 和 DenseNet-169 并结合测试时增强（TTA）的集成模型来检测 IDC。该模型的平衡准确率为 92.70%，F1 得分为 95.70%，优于当前的最先进水平。我们对各种预训练的深度学习模型和预处理方法进行了对比分析，并对测试数据集进行了定性分析。在检测到 IDC 乳腺癌后，对其进行分级以进行进一步治疗非常重要。在我们的研究中，我们还提出了一种使用预训练的 DenseNet-121、DenseNet-201、ResNet-101v2 和 ResNet-50 架构的集成模型来对 IDC 进行分级。该模型从两个验证队列中进行推断。对于补丁级分类，该模型在一个验证队列中对 4×、10×、20×和 40×放大图像的总体准确率分别为 69.31%、75.07%、61.85%和 60.50%，在第二个验证队列中分别为 62.44%、79.14%、76.62%和 71.05%。对于不同的 IDC 数据集，同样的架构进一步得到验证，总体准确率为 90.07%。模型在 IDC 的检测和分级中的性能表明，它们可以帮助病理学家检测和分级疾病。
